Student agency and game-based learning: A study comparing low and high agency

Abstract

A key feature of most computer-based games is agency: the capability for students to make their own decisions in how they play. Agency is assumed to lead to engagement and fun, but may or may not be helpful to learning. While the best learners are often good self-regulated learners, many students are not, only benefiting from instructional choices made for them. In the study presented in this paper, involving a total of 158 fifth and sixth grade students, children played a mathematics learning game called Decimal Point, which helps middle-school students learn decimals. One group of students (79) played and learned with a low-agency version of the game, in which they were guided to play all “mini-games” in a prescribed sequence. The other group of students (79) played and learned with a high-agency version of the game, in which they could choose how many and in what order they would play the mini-games. The results show there were no significant differences in learning or enjoyment across the low and high-agency conditions. A key reason for this may be that students across conditions did not substantially vary in the way they played, perhaps due to the indirect control features present in the game. It may also be the case that the young students who participated in this study did not exercise their agency or self-regulated learning. This work is relevant to the AIED community, as it explores how game-based learning can be adapted. In general, once we know which game and learning features lead to the best learning outcomes, as well as the circumstances that maximize those outcomes, we can better design AI-powered, adaptive games for learning.